REVIEW 4 cited by
Model Checking Access Control Policies: A Case Study using Google Cloud IAM
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Model Checking Access Control Policies: A Case Study using Google Cloud IAM
read the original abstract
Authoring access control policies is challenging and prone to misconfigurations. Access control policies must be conflict-free. Hence, administrators should identify discrepancies between policy specifications and their intended function to avoid violating security principles. This paper aims to demonstrate how to formally verify access control policies. Model checking is used to verify access control properties against policies supported by an access control model. The authors consider Google's Cloud Identity and Access Management (IAM) as a case study and follow NIST's guidelines to verify access control policies automatically. Automated verification using model checking can serve as a valuable tool and assist administrators in assessing the correctness of access control policies. This enables checking violations against security principles and performing security assessments of policies for compliance purposes. The authors demonstrate how to define Google's IAM underlying role-based access control (RBAC) model, specify its supported policies, and formally verify a set of properties through three examples.
Forward citations
Cited by 4 Pith papers
-
AI Native Asset Intelligence
The paper presents a modeling-plus-scoring framework that turns fragmented security signals into stable asset-level importance scores by separating intrinsic exposure from business and data context, evaluated on 131k ...
-
AI Native Asset Intelligence
AI-native asset intelligence framework converts heterogeneous security signals into normalized asset importance scores by separating intrinsic exposure from contextual factors using modeling and deterministic aggregation.
-
Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation
LLMs handle LTL syntax better than semantics, improve with detailed prompts, and perform substantially better when the task is reframed as Python code completion.
-
Compliance Management for Federated Data Processing
A prototype framework collects legal requirements and translates them into machine-actionable policies for federated data processing networks via policy-as-code and LLMs.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.